Introduction to Marketing Analytics

Outline The new realities of marketing decision making Marketing Engineering (ME) The role of models in ME Allegro exercise

Challenges faced by today s marketing decision makers Global, hypercompetitive business environment. More demanding customers served by a greater number of competitors on a global scale. Exploding volume of data We re drowning in data. What we lack are true insights. Need for faster decision making Information overload and lack of time, yet decisions have to be made all the time. Higher standards of accountability Marketing expenditures have to be justified in the same way as other investments.

Need for better marketing decision making Intuitive decision making Instinct, gut feelings, past experience, established practices; In a world characterized by rapid change, information overload, greater accountability, etc. intuition is unlikely to generate superior results; Model- and data-based decision making Marketing Engineering

Marketing Engineering (ME) A systematic approach to harness data and knowledge to drive effective marketing decision making and implementation through a technologyenabled and model-supported interactive decision process. (LRB, p. 2).

Marketing Engineering Introduction to Marketing Analytics Marketing Engineering Marketing Environment Automatic scanning, data entry, subjective interpretation Data Information Insights Decisions Implementation Database management, e.g.., selection, sorting, summarization, report generation Mental models, Decision models Judgment under uncertainty, e.g.., modeling, communication, introspection Financial, human, and other organizational resources

Models are the core of ME: What is a Model? Models are stylized representations of reality that structure our thinking about how the world works; Models indicate which factors should be considered and which factors can be ignored; By focusing on the relevant factors and their interrelationship reality can be simplified; Models are useful because they facilitate top-down processing (as opposed to bottom-up processing);

Issues in using models assembling an arsenal of models for a domain of interest; retrieving relevant mental models in a given situation; being aware of the limitations of mental models (they may overrepresent and underrepresent, or even malrepresent, things); No model is true, but some models are useful.

Types of models Verbal Box and Arrow Graphical Mathematical Sales are a function of advertising Advertising sales S = a + b A + e Sales advertising

Response models in the decision loop Marketing actions (inputs) Competitive actions Observations (outputs) Product design, Price, Advertising, Selling effort, etc. Response Model Awareness Preferences Sales Environmental Conditions Control, Adaptation Objectives Evaluation

Sales Introduction to Marketing Analytics A simple (linear) response model 13200000 Sales as a function of advertising spending 13000000 12800000 12600000 12400000 12200000 12000000 11800000 11600000 11400000 700000 800000 900000 1000000 1100000 1200000 1300000 1400000 1500000 Advertising spending

Sales Introduction to Marketing Analytics 13200000 A simple (linear) response model Actual and predicted sales as a function of advertising spending 13000000 12800000 12600000 12400000 12200000 12000000 11800000 11600000 11400000 700000 800000 900000 1000000 1100000 1200000 1300000 1400000 1500000 Advertising spending Predicted sales Actual sales

A simple (linear) response model SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.88 Standard Error 151514.77 Observations 10 ANOVA df SS MS F Significance F Regression 1 1568596189164 1568596189164 68 0 Residual 8 183653810836 22956726354 Total 9 1752250000000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 9906547.3 292891.1 33.8 0.0 9231139.1 10581955.5 Advertising 2.1 0.3 8.3 0.0 1.5 2.7

Specify the model How to build a model Variables (which ones to include) Relationships, interactions, dynamics (how they are linked) Calibrate the model Statistical estimation with real (historical) data Experimentation Managerial judgments Validate the model Face validity (does it make sense?) Global fit (R², model fit) Variable significance (correct signs, t-tests) Apply the model Ease of use Usefulness

Allegro Resource allocation "smart sheet" exercise Introduction Following are two business plan spreadsheets for the same company, Allegro. You have just been assigned to be the brand manager and must formulate a plan for next year. As you will see, the projected profits for 2006 are higher than the plan for 2007. To make a good impression, you need to develop a plan that will deliver higher profits in 2007. Your job is to use each spreadsheet to determine the optimal level of price, advertising, and selling. How to use this spreadsheet Start with the Simple spreadsheet. It is a traditional spreadsheet. Use this as an aid to help you think about optimal spending levels, and how this might affect market shares and net profits. Go to the second spreadsheet. It is a "smart" spreadsheet where projected market share is a function of price, advertising and distribution spending. If you change the planned price, sales (and contribution margin) will change. Not surprisingly, as you lower price, volume increases, but the contribution margin decreases. As you increase advertising or selling effort sales volume increases. These response functions came from managerial judgment, but could just as easily come from statistical analysis of past data. Assume these estimates are reasonably good; however, change them if you think it is a good idea. Bright yellow indicates a cell you should change. Faded yellow indicates a cell you are allowed to modify. Do this only if you have good reasons to do so.

The Allegro exercise: Simple sheet Allegro Market Simple Sheet This year (current) Next year (projected) Industry Sales, in Units 2,200,000 2,345,700 Company Market Share 3.0% 3.0% Price per Unit $250 $250 Variable Cost per Unit $150 $150 Gross Margin per Unit $100 $100 Sales Volume in Units 66,000 69,678 Sales Revenue $16,500,000 $17,419,532 Gross Contribution Margin $6,600,000 $6,967,813 Overhead $3,500,000 $3,900,000 Net Contribution Margin $3,100,000 $3,067,813 Advertising $900,000 $900,000 Sales Force and Distribution $1,000,000 $1,000,000 Marketing Research $100,000 $100,000 Net Profit $1,100,000 $1,067,813 Marketing Mix Decisions This year (current) Next year (projected) Price per Unit $250 $250 Advertising $900,000 $900,000 Sales Force and Distribution $1,000,000 $1,000,000

The profit equation Profit = Revenues Costs Sales Volume Price Variable Costs Fixed Costs Industry sales Market Share

Questions Using the Simple Sheet, what happens when Allegro cuts advertising and selling effort to 0$ and raises price to $500/unit? Comment. Repeat exercise 1 using the Smart Sheet. Using the Smart Sheet, what is the profit maximizing level of advertising, selling effort and price? (Hint: Requires Solver) Would you recommend the firm implement this policy? Why or why not? What if the firm's goal was not to maximize profit, but to maximize market share while maintaining profit at no lower than last year's level. (Hint: Requires Solver and be sure to start with a feasible level of profit). Compare this policy to the one you found in the previous question. Comment on the strengths and limitations of a response function approach (Smart sheet) like this in practice.

The Allegro exercise: Simple sheet (Q1) Allegro Market Simple Sheet This year (current) Next year (projected) Industry Sales, in Units 2,200,000 2,345,700 a Company Market Share 3.0% 3.0% b Price per Unit $250 $500 c Variable Cost per Unit $150 $150 d Gross Margin per Unit $100 $350 e= c - d Sales Volume in Units 66,000 69,678 f= a x b Sales Revenue $16,500,000 $34,839,064 g= c x f Gross Contribution Margin $6,600,000 $24,387,345 h= e x f Overhead $3,500,000 $3,900,000 i Net Contribution Margin $3,100,000 $20,487,345 j= h - i Advertising $900,000 $0 k Sales Force and Distribution $1,000,000 $0 l Marketing Research $100,000 $100,000 m Net Profit $1,100,000 $20,387,345 n= j - k - l - m Marketing Mix Decisions This year (current) Next year (projected) Price per Unit $250 $500 Advertising $900,000 $0 Sales Force and Distribution $1,000,000 $0

The Allegro exercise: Smart sheet (Q2) Allegro Market Smart Sheet This year (current) Next year (projected) Industry Sales, in Units 2,200,000 2,345,700 Company Market Share 3.0% 0.0% Price per Unit $250 $500 Variable Cost per Unit $150 $150 Gross Margin per Unit $100 $350 Sales Volume in Units 66,000 0 Sales Revenue $16,500,000 $0 Gross Contribution Margin $6,600,000 $0 Overhead $3,500,000 $3,900,000 Net Contribution Margin $3,100,000 -$3,900,000 Advertising $900,000 $0 Sales Force and Distribution $1,000,000 $0 Marketing Research $100,000 $100,000 Net Profit $1,100,000 -$4,000,000 Marketing Mix Decisions This year (current) Next year (projected) Price per Unit $250 $500 Advertising $900,000 $0 Sales Force and Distribution $1,000,000 $0 Managerial Judgmental Inputs What increase in sales will we see with a......1% decrease in price? 2.20%...1% increase in advertising? 0.2%...1% increase in sales effort? 0.1%

The profit equation Profit = Revenues Costs Sales Volume Price Variable Costs Fixed Costs (Advertising, Distribution) (Other Fixed Costs) Industry sales Market Share

Questions Using the Simple Sheet, what happens when Allegro cuts advertising and selling effort to 0$ and raises price to $500/unit? Comment. Repeat exercise 1 using the Smart Sheet. Trial and error using the Smart Sheet. Using the Smart Sheet, what is the profit maximizing level of advertising, selling effort and price? (Hint: Requires Solver) Would you recommend the firm implement this policy? Why or why not? What if the firm's goal was not to maximize profit, but to maximize market share while maintaining profit at no lower than last year's level. (Hint: Requires Solver and be sure to start with a feasible level of profit). Compare this policy to the one you found in the previous question. Comment on the strengths and limitations of a response function approach (Smart sheet) like this in practice.

Using Solver for Optimization

The Allegro exercise: Smart sheet (Q3) Allegro Market Smart Sheet This year (current) Next year (projected) Industry Sales, in Units 2,200,000 2,345,700 a Company Market Share 3.0% 2.7% b Price per Unit $250 $275 c Variable Cost per Unit $150 $150 d Gross Margin per Unit $100 $125 e= c - d Sales Volume in Units 66,000 63,571 f= a x b Sales Revenue $16,500,000 $17,481,989 g= c x f Gross Contribution Margin $6,600,000 $7,946,358 h= e x f Overhead $3,500,000 $3,900,000 i Net Contribution Margin $3,100,000 $4,046,358 j= h - i Advertising $900,000 $1,589,271 k Sales Force and Distribution $1,000,000 $1,033,026 l Marketing Research $100,000 $100,000 m Net Profit $1,100,000 $1,324,061 n= j - k - l - m Marketing Mix Decisions This year (current) Next year (projected) Price per Unit $250 $275 Advertising $900,000 $1,589,271 Sales Force and Distribution $1,000,000 $1,033,026

Using Solver for Optimization

The Allegro exercise: Smart sheet (Q4) Allegro Market Smart Sheet This year (current) Next year (projected) Industry Sales, in Units 2,200,000 2,345,700 Company Market Share 3.0% 3.8% Price per Unit $250 $244 Variable Cost per Unit $150 $150 Gross Margin per Unit $100 $94 Sales Volume in Units 66,000 88,467 Sales Revenue $16,500,000 $21,611,347 Gross Contribution Margin $6,600,000 $8,341,344 Overhead $3,500,000 $3,900,000 Net Contribution Margin $3,100,000 $4,441,344 Advertising $900,000 $1,964,533 Sales Force and Distribution $1,000,000 $1,276,811 Marketing Research $100,000 $100,000 Net Profit $1,100,000 $1,100,000 Marketing Mix Decisions This year (current) Next year (projected) Price per Unit $250 $244 Advertising $900,000 $1,964,533 Sales Force and Distribution $1,000,000 $1,276,811

Team 1 Positioning the Infinity G20 Team 2 Northern Aero CLV Team 3 FLIP Side of Segmentation Team 4 ABB Electric Team 5 Positioning the Infinity G20 Team 6 Durr conjoint Team 7 FLIP Side of Segmentation Team 8 ABB Electric Teams